Int. J. Wavelets Multiresolution Inf. Process.最新文献_第3页

Closed-form shrinkage function based on mixture of Gauss-Laplace distributions for dropping ambient noise 基于高斯-拉普拉斯混合分布的封闭收缩函数去除环境噪声

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-12-01 DOI: 10.1142/s0219691322500618

Kyong-il Kim, Wi-Ung Kwak, Kyong-Hyok Choe

引用次数: 0

The Bessel wavelet transform of distributions in DL2′-type space DL2 '型空间中分布的贝塞尔小波变换

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-12-01 DOI: 10.1142/s021969132250062x

Jay Singh Maurya, S. Upadhyay

引用次数: 0

Learning performance of uncentered kernel-based principal component analysis 基于无中心核的主成分分析的学习性能

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-11-22 DOI: 10.1142/s021969132250059x

Xue Jiang, Hong Sun

引用次数: 0

Standard pairs and construction of multiwavelets using refinement masks satisfying sum rules of order one 满足一阶和规则的多小波的标准对和构造

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-11-22 DOI: 10.1142/s0219691322500606

P. Poornima, Murugesan Kuppusamy

引用次数: 0

Cross-domain residual deep NMF for transfer learning between different hyperspectral image scenes 用于不同高光谱图像场景间迁移学习的跨域残差深度NMF

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-11-21 DOI: 10.1142/s0219691322500461

Ling Lei, Binqian Huang, Minchao Ye, Futian Yao, Y. Qian

{"title":"Cross-domain residual deep NMF for transfer learning between different hyperspectral image scenes","authors":"Ling Lei, Binqian Huang, Minchao Ye, Futian Yao, Y. Qian","doi":"10.1142/s0219691322500461","DOIUrl":"https://doi.org/10.1142/s0219691322500461","url":null,"abstract":"Hyperspectral image (HSI) classification has long been a hot research topic. Most previous researches concentrate on the classification task of a single HSI scene, called single-scene classification. This research focuses on two closely related HSI scenes (called source and target scenes, respectively), and the problem is named cross-scene classification. This paper aims to explore the shared feature sub-space between two HSI scenes. A transfer learning algorithm called cross-domain residual deep nonnegative matrix factorization (CDRDNMF) is proposed. CDRDNMF is a multi-layer architecture consisting of dual-dictionary nonnegative matrix factorization (DDNMF) layers. In each layer, DDNMF is performed on source and target features for domain-invariant feature extraction. Then a data recovery process is completed, and the residual components from the recovery are passed to the next layer after activation. With such a multi-layer architecture, CDRDNMF delivers knowledge transfer and multi-scale feature extraction tasks. The experimental results prove the excellent performance of CDRDNMF on cross-scene classification.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134155523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TFA-CLSTMNN: Novel convolutional network for sound-based diagnosis of COVID-19 TFA-CLSTMNN:基于声音诊断的新型卷积网络

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-11-18 DOI: 10.1142/s0219691322500588

Yuhao He, Xianwei Zheng, Qing Miao

{"title":"TFA-CLSTMNN: Novel convolutional network for sound-based diagnosis of COVID-19","authors":"Yuhao He, Xianwei Zheng, Qing Miao","doi":"10.1142/s0219691322500588","DOIUrl":"https://doi.org/10.1142/s0219691322500588","url":null,"abstract":"The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130701904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Approximation properties of wavelets on p-adic fields p进域上小波的逼近性质

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-11-14 DOI: 10.1142/s0219691322500576

N. Athira, M. C. Lineesh

引用次数: 0

Jump detection in high-frequency financial data using wavelets 基于小波的高频金融数据跳跃检测

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-11-14 DOI: 10.1142/s0219691322500564

Mateus Gonzalez de Freitas Pinto, Guilherme de Oliveira Lima C. Marques, Chang Chiann

引用次数: 0

Differentially private distributed logistic regression with the objective function perturbation 目标函数摄动下的差分私有分布逻辑回归

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-11-10 DOI: 10.1142/s0219691322500436

Haibo Yang, Yulong Ji, Yanfeng Pan, Bin Zou, Yingxiong Fu

{"title":"Differentially private distributed logistic regression with the objective function perturbation","authors":"Haibo Yang, Yulong Ji, Yanfeng Pan, Bin Zou, Yingxiong Fu","doi":"10.1142/s0219691322500436","DOIUrl":"https://doi.org/10.1142/s0219691322500436","url":null,"abstract":"Distributed learning is a very effective divide-and-conquer strategy for dealing with big data. As distributed learning algorithms become more and more mature, network security issues including the risk of privacy disclosure of personal sensitive data, have attracted high attention and vigilance. Differentially private is an important method that maximizes the accuracy of a data query while minimizing the chance of identifying its records when querying from the given data. The known differentially private distributed learning algorithms are based on variable perturbation, but the variable perturbation method may be non-convergence and the experimental results usually have large deviations. Therefore, in this paper, we consider differentially private distributed learning algorithm based on objective function perturbation. We first propose a new distributed logistic regression algorithm based on objective function perturbation (DLR-OFP). We prove that the proposed DLR-OFP satisfies differentially private, and obtain its fast convergence rate by introducing a new acceleration factor for the gradient descent method. The numerical experiments based on benchmark data show that the proposed DLR-OFP algorithm has fast convergence rate and better privacy protection ability.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114188118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective image splicing detection using deep neural network 基于深度神经网络的有效图像拼接检测

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2022-11-04 DOI: 10.1142/s0219691322500515

Suja Priyadharsini Subramoniam, K. K. Devi

引用次数: 0